Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine

By Achmad Widodo, Eric Y. Kim, Jong-Duk Son, Bo-Suk Yang, Andy Chit Chiow Tan, Dong-Sik Gu, Byeong-Keun Choi, Joseph Mathew. Expert systems with applications, 2009

318 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/d41edd507520c99fa0c5a89bf7b1cd69c2bff380

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Dong Yang
2. Wei Qiao
3. Weiting Zhang
4. Dingguo Lu
5. Hongchao Wang

In Mechanical Engineering

1. Dong Yang
2. Wei Qiao
3. Weiting Zhang
4. Dingguo Lu
5. Hongchao Wang

Citations in the corpus

• Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, methodology): “[69] focused on fault diagnosis of bearings by using a multiclass RVM and an SVM; the data were acquired from the low-speed bearing test rig by acoustic emission sensors and accelerometers.”
• A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part II: Signals and Signal Processing Methods (2015, methodology): “damage and monitor its development to a failure [10].”
• Fine tuning support vector machines for short-term wind speed forecasting (2011, methodology): “In recent years, support vector machines (SVM), a novel and powerful machine learning tool, has been successfully used for time-series prediction with satisfactory prediction results in various fields [16–21].”
• A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part I: Components and Subsystems (2015, methodology): “AE signals have also been used for CMFD of bearings [39].”